[mlir][python] Add pythonic interface for GPUFuncOp

mlir/python/mlir/dialects/gpu/__init__.py

@@ -3,5 +3,167 @@
 #  SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 
 from .._gpu_ops_gen import *
+from .._gpu_ops_gen import _Dialect
 from .._gpu_enum_gen import *
 from ..._mlir_libs._mlirDialectsGPU import *
+from typing import Callable, Sequence, Union, Optional, List
+
+try:
+    from ...ir import (
+        FunctionType,
+        TypeAttr,
+        StringAttr,
+        UnitAttr,
+        Block,
+        InsertionPoint,
+        ArrayAttr,
+        Type,
+        DictAttr,
+        Attribute,
+        DenseI32ArrayAttr,
+    )
+    from .._ods_common import (
+        get_default_loc_context as _get_default_loc_context,
+        _cext as _ods_cext,
+    )
+except ImportError as e:
+    raise RuntimeError("Error loading imports from extension module") from e
+
+
+KERNEL_ATTRIBUTE_NAME = "gpu.kernel"
+KNOWN_BLOCK_SIZE_ATTRIBUTE_NAME = "gpu.known_block_size"
+KNOWN_GRID_SIZE_ATTRIBUTE_NAME = "gpu.known_grid_size"
+
+FUNCTION_TYPE_ATTRIBUTE_NAME = "function_type"
+SYM_NAME_ATTRIBUTE_NAME = "sym_name"
+ARGUMENT_ATTRIBUTE_NAME = "arg_attrs"
+RESULT_ATTRIBUTE_NAME = "res_attrs"
+
+
+@_ods_cext.register_operation(_Dialect, replace=True)
+class GPUFuncOp(GPUFuncOp):
+    __doc__ = GPUFuncOp.__doc__
+
+    def __init__(
+        self,
+        function_type: Union[FunctionType, TypeAttr],
+        sym_name: Optional[Union[str, StringAttr]] = None,
+        kernel: Optional[bool] = None,
+        body_builder: Optional[Callable[[GPUFuncOp], None]] = None,
+        known_block_size: Optional[Union[List[int], DenseI32ArrayAttr]] = None,
+        known_grid_size: Optional[Union[List[int], DenseI32ArrayAttr]] = None,
+        *args,
+        loc=None,
+        ip=None,
+        **kwargs,
+    ):
+        """
+        Create a GPUFuncOp with the provided `function_type`, `sym_name`, `kernel`, `body_builder`, `known_block_size`, and `known_grid_size`.
+        - `function_type` is a FunctionType or a TypeAttr.
+        - `sym_name` is a string or a StringAttr representing the function name.
+        - `kernel` is a boolean representing whether the function is a kernel.
+        - `body_builder` is an optional callback. When provided, a new entry block
+          is created and the callback is invoked with the new op as argument within
+          an InsertionPoint context already set for the block. The callback is
+          expected to insert a terminator in the block.
+        - `known_block_size` is an optional list of integers or a DenseI32ArrayAttr representing the known block size.
+        - `known_grid_size` is an optional list of integers or a DenseI32ArrayAttr representing the known grid size.
+        """
+        function_type = (
+            TypeAttr.get(function_type)
+            if not isinstance(function_type, TypeAttr)
+            else function_type
+        )
+        super().__init__(function_type, *args, loc=loc, ip=ip, **kwargs)
+        if sym_name is not None:
+            self.attributes[SYM_NAME_ATTRIBUTE_NAME] = StringAttr.get(str(sym_name))
+        if kernel:
+            self.attributes[KERNEL_ATTRIBUTE_NAME] = UnitAttr.get()
+        if body_builder is not None:
+            with InsertionPoint(self.add_entry_block()):
+                body_builder(self)
+        if known_block_size is not None:
+            if isinstance(known_block_size, list):
+                self.attributes[KNOWN_BLOCK_SIZE_ATTRIBUTE_NAME] = (
+                    DenseI32ArrayAttr.get(known_block_size)
+                )
+            else:
+                self.attributes[KNOWN_BLOCK_SIZE_ATTRIBUTE_NAME] = known_block_size
+        if known_grid_size is not None:
+            if isinstance(known_grid_size, list):
+                self.attributes[KNOWN_GRID_SIZE_ATTRIBUTE_NAME] = DenseI32ArrayAttr.get(
+                    known_grid_size
+                )
+            else:
+                self.attributes[KNOWN_GRID_SIZE_ATTRIBUTE_NAME] = known_grid_size
+
+    @property
+    def type(self) -> FunctionType:
+        return FunctionType(
+            TypeAttr(self.attributes[FUNCTION_TYPE_ATTRIBUTE_NAME]).value
+        )
+
+    @property
+    def name(self) -> StringAttr:
+        return StringAttr(self.attributes[SYM_NAME_ATTRIBUTE_NAME])
+
+    @property
+    def is_kernel(self) -> bool:
+        return KERNEL_ATTRIBUTE_NAME in self.attributes
+
+    @property
+    def known_block_size(self) -> Optional[DenseI32ArrayAttr]:
+        if KNOWN_BLOCK_SIZE_ATTRIBUTE_NAME not in self.attributes:
+            return None
+        return self.attributes[KNOWN_BLOCK_SIZE_ATTRIBUTE_NAME]
+
+    @property
+    def known_grid_size(self) -> Optional[DenseI32ArrayAttr]:
+        if KNOWN_GRID_SIZE_ATTRIBUTE_NAME not in self.attributes:
+            return None
+        return self.attributes[KNOWN_GRID_SIZE_ATTRIBUTE_NAME]
+
+    def add_entry_block(self) -> Block:
+        function_type = self.type
+        return self.body.blocks.append(
+            *function_type.inputs,
+            arg_locs=[self.location for _ in function_type.inputs],
+        )
+
+    @property
+    def entry_block(self) -> Block:
+        return self.body.blocks[0]
+
+    @property
+    def arguments(self) -> Sequence[Type]:
+        return self.type.inputs
+
+    @property
+    def arg_attrs(self):
+        if ARGUMENT_ATTRIBUTE_NAME not in self.attributes:
+            return ArrayAttr.get([DictAttr.get({}) for _ in self.type.inputs])
+        return ArrayAttr(self.attributes[ARGUMENT_ATTRIBUTE_NAME])
+
+    @arg_attrs.setter
+    def arg_attrs(self, attribute: Union[ArrayAttr, list[Attribute]]):
+        if isinstance(attribute, ArrayAttr):
+            self.attributes[ARGUMENT_ATTRIBUTE_NAME] = attribute
+        else:
+            self.attributes[ARGUMENT_ATTRIBUTE_NAME] = ArrayAttr.get(
+                attribute, context=self.context
+            )
+
+    @property
+    def result_attrs(self) -> Optional[ArrayAttr]:
+        if RESULT_ATTRIBUTE_NAME not in self.attributes:
+            return ArrayAttr.get([DictAttr.get({}) for _ in self.type.results])
+        return self.attributes[RESULT_ATTRIBUTE_NAME]
+
+    @result_attrs.setter
+    def result_attrs(self, attribute: Union[ArrayAttr, list[Attribute]]):
+        if isinstance(attribute, ArrayAttr):
+            self.attributes[RESULT_ATTRIBUTE_NAME] = attribute
+        else:
+            self.attributes[RESULT_ATTRIBUTE_NAME] = ArrayAttr.get(
+                attribute, context=self.context
+            )

mlir/test/python/dialects/gpu/dialect.py

@@ -1,6 +1,7 @@
 # RUN: %PYTHON %s | FileCheck %s
 
 from mlir.ir import *
+import mlir.ir as ir
 import mlir.dialects.gpu as gpu
 import mlir.dialects.gpu.passes
 from mlir.passmanager import *
@@ -64,3 +65,74 @@ def testObjectAttr():
     # CHECK: #gpu.object<#nvvm.target, kernels = <[#gpu.kernel_metadata<"kernel", () -> ()>]>, "BC\C0\DE5\14\00\00\05\00\00\00b\0C0$MY\BEf">
     print(o)
     assert o.kernels == kernelTable
+
+
+# CHECK-LABEL: testGPUFuncOp
+@run
+def testGPUFuncOp():
+    assert gpu.GPUFuncOp.__doc__ is not None
+    module = Module.create()
+    with InsertionPoint(module.body):
+        gpu_module_name = StringAttr.get("gpu_module")
+        gpumodule = gpu.GPUModuleOp(gpu_module_name)
+        block = gpumodule.bodyRegion.blocks.append()
+
+        def builder(func: gpu.GPUFuncOp) -> None:
+            gpu.GlobalIdOp(gpu.Dimension.x)
+            gpu.ReturnOp([])
+
+        with InsertionPoint(block):
+            name = StringAttr.get("kernel0")
+            func_type = ir.FunctionType.get(inputs=[], results=[])
+            type_attr = TypeAttr.get(func_type)
+            func = gpu.GPUFuncOp(type_attr, name)
+            func.attributes[gpu.SYM_NAME_ATTRIBUTE_NAME] = name
+            func.attributes[gpu.KERNEL_ATTRIBUTE_NAME] = UnitAttr.get()
+            block = func.body.blocks.append()
+            with InsertionPoint(block):
+                builder(func)
+
+            func = gpu.GPUFuncOp(
+                func_type,
+                sym_name="kernel1",
+                kernel=True,
+                body_builder=builder,
+                known_block_size=[1, 2, 3],
+                known_grid_size=DenseI32ArrayAttr.get([4, 5, 6]),
+            )
+
+            assert func.name.value == "kernel1"
+            assert func.arg_attrs == ArrayAttr.get([])
+            assert func.result_attrs == ArrayAttr.get([])
+            assert func.arguments == []
+            assert func.entry_block == func.body.blocks[0]
+            assert func.is_kernel
+            assert func.known_block_size == DenseI32ArrayAttr.get([1, 2, 3])
+            assert func.known_grid_size == DenseI32ArrayAttr.get([4, 5, 6])
+
+            func = gpu.GPUFuncOp(
+                func_type,
+                sym_name="non_kernel_func",
+                body_builder=builder,
+            )
+            assert not func.is_kernel
+            assert func.known_block_size is None
+            assert func.known_grid_size is None
+
+    print(module)
+
+    # CHECK: gpu.module @gpu_module
+    # CHECK: gpu.func @kernel0() kernel {
+    # CHECK:   %[[VAL_0:.*]] = gpu.global_id  x
+    # CHECK:   gpu.return
+    # CHECK: }
+    # CHECK: gpu.func @kernel1() kernel attributes
+    # CHECK-SAME: gpu.known_block_size = array<i32: 1, 2, 3>
+    # CHECK-SAME: gpu.known_grid_size = array<i32: 4, 5, 6>
+    # CHECK:   %[[VAL_0:.*]] = gpu.global_id  x
+    # CHECK:   gpu.return
+    # CHECK: }
+    # CHECK: gpu.func @non_kernel_func() {
+    # CHECK:   %[[VAL_0:.*]] = gpu.global_id  x
+    # CHECK:   gpu.return
+    # CHECK: }